干旱区生态环境知识资源中心(Arid): Hydrological stream flow modelling using soil and water assessment tool (SWAT) and neural networks (NNs) for the Limkheda watershed, Gujarat, India

Arid

DOI	10.1007/s40808-017-0323-y
	Hydrological stream flow modelling using soil and water assessment tool (SWAT) and neural networks (NNs) for the Limkheda watershed, Gujarat, India
	Makwana, Jaydip J.; Tiwari, Mukesh K.
通讯作者	Tiwari, MK
来源期刊	MODELING EARTH SYSTEMS AND ENVIRONMENT
ISSN	2363-6203
EISSN	2363-6211
出版年	2017
卷号	3 期号:2 页码:635-645
英文摘要	Investigation of continuous daily streamflow based on rainfall in arid and semi-arid region is challenging, particularly when climate records are limited, time consuming or unavailable. A calibrated and validated model to simulate hydrological processes will be a great help to the concerned watershed management. In this study the accuracy of the Soil and Water Assessment Tools (SWAT) and Neural Networks (NNs) are compared to perform continuous simulation of runoff in a hilly and agricultural watershed, named Limkheda watershed of Gujarat, India. We used the remote sensing data (SRTM-DEM imagery, soil maps and land use/cover classification from LISS-III imagery, etc), climatic and discharge data are used as primary inputs for SWAT models, whereas only climatic data and discharge data were used for NN model setup. The climatic and observed streamflow data from 2 years (2009-2010) were used for calibration and another 2 years (2011-2012) data were used for model validation. To examine the efficiency of both models five performance indices were applied. In the present study, performance of the NNs model was found better than SWAT model for simulating surface runoff from the watershed based on calibration and validation results. It is found in this study that SWAT model provides a better description of water balance of the watershed, whereas NN models present the surface runoff at the outlet without any explicit consideration of different components of the hydrologic cycle.
英文关键词	Hydrological modelling Surface runoff Remote sensing and GIS SWAT Neural networks
类型	Article
语种	英语
收录类别	ESCI
WOS记录号	WOS:000432244700011
WOS关键词	RAINFALL-RUNOFF ; PREDICTION ; BASIN ; RIVER ; QUALITY ; CALIBRATION ; PATTERNS ; CLIMATE ; IMPACT ; LOAD
WOS类目	Environmental Sciences
WOS研究方向	Environmental Sciences & Ecology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/332580
作者单位	[Makwana, Jaydip J.; Tiwari, Mukesh K.] Anand Agr Univ, Coll Agr Engn & Technol, Godhra, Gujarat, India
推荐引用方式 GB/T 7714	Makwana, Jaydip J.,Tiwari, Mukesh K.. Hydrological stream flow modelling using soil and water assessment tool (SWAT) and neural networks (NNs) for the Limkheda watershed, Gujarat, India[J],2017,3(2):635-645.
APA	Makwana, Jaydip J.,&Tiwari, Mukesh K..(2017).Hydrological stream flow modelling using soil and water assessment tool (SWAT) and neural networks (NNs) for the Limkheda watershed, Gujarat, India.MODELING EARTH SYSTEMS AND ENVIRONMENT,3(2),635-645.
MLA	Makwana, Jaydip J.,et al."Hydrological stream flow modelling using soil and water assessment tool (SWAT) and neural networks (NNs) for the Limkheda watershed, Gujarat, India".MODELING EARTH SYSTEMS AND ENVIRONMENT 3.2(2017):635-645.